20 years of turbo coding and energy-aware design guidelines for energy-constrained wireless applications

During the last two decades, wireless communication has been revolutionized by near-capacity error-correcting codes (ECCs), such as turbo codes (TCs), which offer a lower bit error ratio (BER) than their predecessors, without requiring an increased transmission energy consumption (EC). Hence, TCs have found widespread employment in spectrum-constrained wireless communication applications, such as cellular telephony, wireless local area network, and broadcast systems. Recently, however, TCs have also been considered for energy-constrained wireless communication applications, such as wireless sensor networks and the `Internet of Things.' In these applications, TCs may also be employed for reducing the required transmission EC, instead of improving the BER. However, TCs have relatively high computational complexities, and hence, the associated signal-processing-related ECs are not insignificant. Therefore, when parameterizing TCs for employment in energy-constrained applications, both the processing EC and the transmission EC must be jointly considered. In this tutorial, we investigate holistic design methodologies conceived for this purpose. We commence by introducing turbo coding in detail, highlighting the various parameters of TCs and characterizing their impact on the encoded bit rate, on the radio frequency bandwidth requirement, on the transmission EC and on the BER. Following this, energy-efficient TC decoder application-specific integrated circuit (ASIC) architecture designs are exemplified, and the processing EC is characterized as a function of the TC parameters. Finally, the TC parameters are selected in order to minimize the sum of the processing EC and the transmission EC

Presentation of an Algorithm for Secure Data Transmission based on Optimal Route Selection during Electromagnetic Interference Occurrence

This paper proposes a comprehensive algorithm for secure data transmission via communication conductors considering route optimization, shielding and data authentication. Using of appropriate coding method causes more efficiency for suggested algorithm during electromagnetic field attack occurrence. In this paper, MOM simulation via FIKO software is done for field distribution. Due to critical situation of malfunctioning of data transferring, appropriate shield is designed and examined by shielding effectiveness (SE) criterion resulted of MOM simulation; finally to achieve reliability of data security, MAC hash function is used for space with field attack probability, turbo code is employed

Extrinsic information transfer charts for characterizing the iterative decoding convergence of fully parallel turbo decoders

Fully parallel turbo decoders (FPTDs) have been shown to offer a more-than-sixfold processing throughput and latency improvement over the conventional logarithmic Bahl–Cocke–Jelinek–Raviv (Log-BCJR) turbo decoders. Rather than requiring hundreds or even thousands of time periods to decode each frame, such as the conventional Log-BCJR turbo decoders, the FPTD completes each decoding iteration using only one or two time periods, although up to six times as many decoding iterations are required to achieve the same error correction performance. Until now, it has not been possible to explain this increased iteration requirement using an extrinsic information transfer (EXIT) chart analysis, since the two component decoders are not alternately operated in the FPTD. Hence, in this paper, we propose a novel EXIT chart technique for characterizing the iterative exchange of not only extrinsic logarithmic likelihood ratios in the FPTD, but also the iterative exchange of extrinsic state metrics. In this way, the proposed technique can accurately predict the number of decoding iterations required for achieving iterative decoding convergence, as confirmed by the Monte Carlo simulation. The proposed technique offers new insights into the operation of FPTDs, which will facilitate improved designs in the future, in the same way as the conventional EXIT charts have enhanced the design and understanding of the conventional Log-BCJR turbo decoder

20 Years of Turbo Coding and Energy-Aware Design Guidelines for Energy-Constrained Wireless Applications

International audienceDuring the last two decades, wireless communication has been revolutionized by near-capacity error-correcting codes (ECCs), such as turbo codes (TCs), which offer a lower bit error ratio (BER) than their predecessors, without requiring an increased transmission energy consumption (EC). Hence, TCs have found widespread employment in spectrum-constrained wireless communication applications, such as cellular telephony, wireless local area network, and broadcast systems. Recently, however, TCs have also been considered for energy-constrained wireless communication applications, such as wireless sensor networks and the ‘Internet of Things.' In these applications, TCs may also be employed for reducing the required transmission EC, instead of improving the BER. However, TCs have relatively high computational complexities, and hence, the associated signal-processing-related ECs are not insignificant. Therefore, when parameterizing TCs for employment in energy-constrained applications, both the processing EC and the transmission EC must be jointly considered. In this tutorial, we investigate holistic design methodologies conceived for this purpose. We commence by introducing turbo coding in detail, highlighting the various parameters of TCs and characterizing their impact on the encoded bit rate, on the radio frequency bandwidth requirement, on the transmission EC and on the BER. Following this, energy-efficient TC decoder application-specific integrated circuit (ASIC) architecture designs are exemplified, and the processing EC is characterized as a function of the TC parameters. Finally, the TC parameters are selected in order to minimize the sum of the processing EC and the transmission EC

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment